1. Field of Disclosure
The present disclosure generally relates to a context-based media file annotation recommendation system for annotating stock photography media files that presents a list of recommended annotations based on a selected set of similar annotations and a selected set of similar media files.
2. Brief Description of Related Art
In recent years, a number of large databases of digital images have been made accessible via the Internet. Typically, searchers looking for a specific digital image employ an image retrieval system for browsing, searching and retrieving images from the image databases. Most traditional image retrieval systems utilize some method of adding metadata to the images such as captioning, keywords, or descriptions and the like.
Subsequently, image retrieval can be performed by searching for text strings appearing in the metadata. Searching a particular image from a large image database via image retrieval systems can be challenging at times. For most large-scale image retrieval systems, performance may depend upon the accuracy of the image metadata. Although the performance of the content-based image retrieval systems has significantly improved in recent years, typically image contributors may still be required to provide appropriate keywords or tags that describe a particular image.
Previous work has explored methods for circumventing this problem. One area where image tag recommendation remains underexplored is in the context of online stock photography. The application of tag recommendation techniques to online stock photography has not yet been explored.
One shortcoming of co-occurrence based tag recommenders such as tag recommender 340 depicted in FIG. 3 is that the tag recommenders are purely text-based. In other words, the alphanumeric text string of the selected similar tags 330 that are extracted from the tag co-occurrence matrix 310 is, at least in part, identical to the alphanumeric text string of the image tag input 300. The conventional tag co-occurrence based recommenders neither capture image context information nor consider non-textual similarities between images.
As the name suggests, the conventional tag co-occurrence based recommenders may only consider text strings of image tags and identify similarities between the text strings of image tags. Besides image tags, images have many other attributes such as image type, image color, image texture, image content, image context, image source, image ratings, media type and the like. These non-textual similarities can be of vital importance in generating succinct image tags that are so crucial in generating stock photography revenue. Given that non-textual similarities between images can play an important role in suggesting appropriate and concise tags, a novel recommender is needed to cure the infirmity.